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Toward Clinically Actionable Machine Learning and Artificial Intelligence Algorithms in Acute Leukemia: A Systematic

Jean Mg Sabile¹, Ping Zhang^2,3, Anil V Parwani⁴

¹Knight Cancer Institute, Division of Hematology & Oncology, Oregon Health & Sciences University, Portland, Oregon, USA.

Acta Haematologica

|July 24, 2025

Summary

This summary is machine-generated.

Artificial intelligence and machine learning (AI/ML) offer new strategies for treating acute myeloid leukemia (AML). These advanced AI/ML tools show promise in improving diagnostics, risk stratification, and outcomes for AML patients.

Keywords:

Acute leukemia Artificial intelligence Leukemia Machine learning Neural network

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Pre-clinical Evaluation of Tyrosine Kinase Inhibitors for Treatment of Acute Leukemia

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Published on: September 18, 2013

Area of Science:

Hematology
Computational Biology
Oncology

Background:

Acute myeloid leukemia (AML) presents significant challenges with high relapse rates and poor survival.
Current treatments for AML have limitations, creating an unmet need for improved long-term survival and reduced toxicity.
Artificial intelligence and machine learning (AI/ML) are emerging as powerful tools to address these clinical challenges in AML.

Purpose of the Study:

To systematically review the application of AI/ML in acute myeloid leukemia.
To describe the evolution of AI/ML tools and their clinical relevance in AML.
To highlight the potential of contemporary AI/ML algorithms in addressing AML-related problems.

Main Methods:

A systematic narrative review of 426 publications.
Publications were selected based on their focus on the intersection of AML and AI/ML.
The review period spanned from January 1, 2010, to July 30, 2024.

Main Results:

The evolution of AI/ML tools in AML was analyzed, distinguishing between early and contemporary algorithms (e.g., generative adversarial networks, transformer-based algorithms).
Contemporary AI/ML algorithms are being utilized for diagnostic challenges, molecular risk stratification, and clinical outcome prediction in AML.
The review demonstrates the increasing integration and impact of AI/ML in various aspects of AML management.

Conclusions:

AI/ML represents a promising frontier for addressing clinical challenges in AML.
Further utilization of AI/ML is recommended, particularly in the context of allogeneic stem cell transplantation.
AI/ML holds potential for improving patient outcomes and treatment strategies in acute myeloid leukemia.